The student model in this work is built on the wide ResNet architecture presented in [1]}. The network is comprised of 16 residual blocks. Dilated convolution [2]} is used to increase the receptive field without decreasing the resolution. The output from the network is bilinearly upsampled to the initial image resolution. The loss function used is bootstrapped cross entropy [3]}, which helps with class imbalance. It computes the cross entropy loss from a fraction of the hardest pixels. Pre-trained weights on PASCAL dataset for objectness is used from [4]}, to help the network generalize to different objects in the scene. Then it is trained on DAVIS training set, the student model without adaptation is denoted as the baseline model throughout the paper.
